Article
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Aircraft Target Interpretation Based on SAR Images
Version 1
: Received: 9 August 2023 / Approved: 9 August 2023 / Online: 10 August 2023 (10:45:52 CEST)
A peer-reviewed article of this Preprint also exists.
Wang, X.; Hong, W.; Liu, Y.; Hu, D.; Xin, P. Aircraft Target Interpretation Based on SAR Images. Appl. Sci. 2023, 13, 10023. Wang, X.; Hong, W.; Liu, Y.; Hu, D.; Xin, P. Aircraft Target Interpretation Based on SAR Images. Appl. Sci. 2023, 13, 10023.
Abstract
Synthetic Aperture Radar (SAR) is an active sensor that uses microwave for sense, it is unrestricted by weather and illumination conditions, and it can observe targets all day and weather. Aircraft targets are important monitoring objects in military and civilian fields, and how to efficiently detect and recognize aircraft targets is an important topic in the field of SAR image interpretation. Based on the features of SAR images, such as complex background, high resolution, and multi-scale, we proposed an improved method based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion, which is applied to the shallow layer of the backbone of YOLOv5s, it can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four de-coupled detection heads to replace the original part in YOLOv5s, which can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multi methods of data augmentation, which can enhance the diversity and generalization of the network. Fourthly, this paper proposed the K-means++ to re-place the original K-means to improve the network convergence speed and detection accuracy. Finally, Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.
Keywords
YOLOv5s, attention mechanism, decoupled detection head, K-means++, aircraft detection, SAR image.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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